code: def integrate_box_1d(self, low, high):
      if self.d != 1:
          raise ValueError("integrate_box_1d() only handles 1D pdfs")
      if jnp.ndim(low) != 0 or jnp.ndim(high) != 0:
          raise ValueError(
              "the limits of integration in integrate_box_1d must be scalars"
          )
      sigma = jnp.squeeze(jnp.sqrt(self.covariance))
      low = jnp.squeeze((low - self.dataset) / sigma)
      high = jnp.squeeze((high - self.dataset) / sigma)
      return jnp.sum(
          self.weights * (special.ndtr(high) - special.ndtr(low))
      )
  
imports: from dataclasses import dataclass
  from functools import partial
  from typing import Any
  import numpy
  import scipy.stats
  import jax.numpy
  from jax import jit, lax, random, vmap
  from jax._src.numpy.util import check_arraylike, promote_dtypes_inexact, _wraps
  from jax._src.tree_util import register_pytree_node_class
  from jax.scipy import linalg, special
label_desc: Use image node_id 10 for calling the gaussian_kde obj's underlying member method code with example usage: obj.integrate_box_1d(low, high) and returns: jnp